毕业设计MATLAB_多光谱图像特征点.zip

**NEW RELATED ARTICLES (Better results)** =============== 1. LGHD: A feature descriptor for matching across non-linear intensity variations code_url: https://github.com/ngunsu/LGHD 2. Learning cross-spectral similarity measures with deep convolutional neural networks code_url: https://github.com/ngunsu/lcsis 3. Cross-Spectral Local Descriptors via Quadruplet Network code_url: https://github.com/ngunsu/qnet ##EOH-SIFT Matlab Code =============== **Article abstract**: This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art. **Article url**: http://www.mdpi.com/1424-8220/12/9/12661 **Bibtex** ``` @Article{s120912661, AUTHOR = {Aguilera, Cristhian and Barrera, Fernando and Lumbreras, Felipe and Sappa, Angel D. and Toledo, Ricardo}, TITLE = {Multispectral Image Feature Points}, JOURNAL = {Sensors}, VOLUME = {12}, YEAR = {2012}, NUMBER = {9}, PAGES = {12661--12672}, URL = {http://www.mdpi.com/1424-8220/12/9/12661}, PubMedID = {23112736}, ISSN = {1424-8220}, DOI = {10.3390/s120912661} } ```